Overview

Dataset statistics

Number of variables20
Number of observations494020
Missing cells0
Missing cells (%)0.0%
Duplicate rows450543
Duplicate rows (%)91.2%
Total size in memory75.4 MiB
Average record size in memory160.0 B

Variable types

NUM15
BOOL3
CAT2

Reproduction

Analysis started2020-08-25 01:27:14.151707
Analysis finished2020-08-25 01:28:49.570067
Duration1 minute and 35.42 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Atr-19 has constant value "0" Constant
Dataset has 450543 (91.2%) duplicate rows Duplicates
Atr-25 is highly correlated with Atr-24High correlation
Atr-24 is highly correlated with Atr-25High correlation
Atr-39 is highly correlated with Atr-40 and 1 other fieldsHigh correlation
Atr-40 is highly correlated with Atr-39 and 1 other fieldsHigh correlation
Atr-26 is highly correlated with Atr-40 and 1 other fieldsHigh correlation
Atr-15 is highly correlated with Atr-12High correlation
Atr-12 is highly correlated with Atr-15High correlation
target is highly correlated with Atr-35High correlation
Atr-35 is highly correlated with targetHigh correlation
Atr-4 is highly skewed (γ1 = 699.2124433) Skewed
Atr-12 is highly skewed (γ1 = 417.5298055) Skewed
Atr-9 is highly skewed (γ1 = 32.62911204) Skewed
Atr-15 is highly skewed (γ1 = 417.0654138) Skewed
Atr-16 is highly skewed (γ1 = 192.334571) Skewed
Atr-24 has 404786 (81.9%) zeros Zeros
Atr-25 has 405685 (82.1%) zeros Zeros
Atr-4 has 115342 (23.3%) zeros Zeros
Atr-40 has 459804 (93.1%) zeros Zeros
Atr-39 has 458791 (92.9%) zeros Zeros
Atr-29 has 382020 (77.3%) zeros Zeros
Atr-26 has 464947 (94.1%) zeros Zeros
Atr-12 has 491796 (99.5%) zeros Zeros
Atr-9 has 490828 (99.4%) zeros Zeros
Atr-15 has 493435 (99.9%) zeros Zeros
Atr-16 has 493755 (99.9%) zeros Zeros
Atr-35 has 142859 (28.9%) zeros Zeros
Atr-34 has 347030 (70.2%) zeros Zeros

Variables

Atr-24
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count92
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17668701671997084
Minimum0.0
Maximum1.0
Zeros404786
Zeros (%)81.9%
Memory size3.8 MiB
2020-08-25T01:28:49.625456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3807172587
Coefficient of variation (CV)2.154755147
Kurtosis0.885489854
Mean0.1766870167
Median Absolute Deviation (MAD)0
Skewness1.697594834
Sum87286.92
Variance0.1449456311
2020-08-25T01:28:49.743498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
040478681.9%
 
18653717.5%
 
0.993110.1%
 
0.08155< 0.1%
 
0.05150< 0.1%
 
0.07129< 0.1%
 
0.06129< 0.1%
 
0.14118< 0.1%
 
0.04115< 0.1%
 
0.01109< 0.1%
 
0.09102< 0.1%
 
0.1101< 0.1%
 
0.0392< 0.1%
 
0.1190< 0.1%
 
0.1388< 0.1%
 
0.587< 0.1%
 
0.1282< 0.1%
 
0.269< 0.1%
 
0.2567< 0.1%
 
0.0262< 0.1%
 
0.1760< 0.1%
 
0.3351< 0.1%
 
0.1544< 0.1%
 
0.2237< 0.1%
 
0.1834< 0.1%
 
Other values (67)4150.1%
 
ValueCountFrequency (%) 
040478681.9%
 
0.01109< 0.1%
 
0.0262< 0.1%
 
0.0392< 0.1%
 
0.04115< 0.1%
 
0.05150< 0.1%
 
0.06129< 0.1%
 
0.07129< 0.1%
 
0.08155< 0.1%
 
0.09102< 0.1%
 
ValueCountFrequency (%) 
18653717.5%
 
0.993110.1%
 
0.9818< 0.1%
 
0.9710< 0.1%
 
0.968< 0.1%
 
0.956< 0.1%
 
0.946< 0.1%
 
0.934< 0.1%
 
0.922< 0.1%
 
0.912< 0.1%
 

Atr-13
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
493965
1
 
55
ValueCountFrequency (%) 
0493965> 99.9%
 
155< 0.1%
 

Atr-8
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
494016
1
 
2
3
 
1
2
 
1
ValueCountFrequency (%) 
0494016> 99.9%
 
12< 0.1%
 
31< 0.1%
 
21< 0.1%
 
2020-08-25T01:28:51.988134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0494016> 99.9%
 
12< 0.1%
 
21< 0.1%
 
31< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number494020100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0494016> 99.9%
 
12< 0.1%
 
21< 0.1%
 
31< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common494020100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0494016> 99.9%
 
12< 0.1%
 
21< 0.1%
 
31< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII494020100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0494016> 99.9%
 
12< 0.1%
 
21< 0.1%
 
31< 0.1%
 

Atr-25
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count51
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17660916562082507
Minimum0.0
Maximum1.0
Zeros405685
Zeros (%)82.1%
Memory size3.8 MiB
2020-08-25T01:28:52.119196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3810168872
Coefficient of variation (CV)2.157401548
Kurtosis0.8821925693
Mean0.1766091656
Median Absolute Deviation (MAD)0
Skewness1.697204063
Sum87248.46
Variance0.1451738684
2020-08-25T01:28:52.254960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
040568582.1%
 
18705217.6%
 
0.03139< 0.1%
 
0.04120< 0.1%
 
0.05109< 0.1%
 
0.0698< 0.1%
 
0.0284< 0.1%
 
0.578< 0.1%
 
0.0873< 0.1%
 
0.0768< 0.1%
 
0.2556< 0.1%
 
0.3355< 0.1%
 
0.1751< 0.1%
 
0.0948< 0.1%
 
0.146< 0.1%
 
0.245< 0.1%
 
0.1143< 0.1%
 
0.1243< 0.1%
 
0.1434< 0.1%
 
0.0110< 0.1%
 
0.679< 0.1%
 
0.926< 0.1%
 
0.186< 0.1%
 
0.945< 0.1%
 
0.955< 0.1%
 
Other values (26)52< 0.1%
 
ValueCountFrequency (%) 
040568582.1%
 
0.0110< 0.1%
 
0.0284< 0.1%
 
0.03139< 0.1%
 
0.04120< 0.1%
 
0.05109< 0.1%
 
0.0698< 0.1%
 
0.0768< 0.1%
 
0.0873< 0.1%
 
0.0948< 0.1%
 
ValueCountFrequency (%) 
18705217.6%
 
0.955< 0.1%
 
0.945< 0.1%
 
0.932< 0.1%
 
0.926< 0.1%
 
0.913< 0.1%
 
0.91< 0.1%
 
0.884< 0.1%
 
0.861< 0.1%
 
0.852< 0.1%
 

Atr-4
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count3300
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.61574430185
Minimum0
Maximum693375640
Zeros115342
Zeros (%)23.3%
Memory size3.8 MiB
2020-08-25T01:28:52.388688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median520
Q31032
95-th percentile1032
Maximum693375640
Range693375640
Interquartile range (IQR)987

Descriptive statistics

Standard deviation988219.1012
Coefficient of variation (CV)326.6175168
Kurtosis490583.3526
Mean3025.615744
Median Absolute Deviation (MAD)512
Skewness699.2124433
Sum1494714690
Variance9.76576992e+11
2020-08-25T01:28:52.521287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
103222803546.2%
 
011534223.3%
 
5205277410.7%
 
10573701.5%
 
14727250.6%
 
5454021430.4%
 
14620330.4%
 
4210690.2%
 
810450.2%
 
289840.2%
 
1459170.2%
 
308960.2%
 
448900.2%
 
468370.2%
 
3347740.2%
 
457360.1%
 
2176150.1%
 
2155960.1%
 
2165960.1%
 
2095850.1%
 
2215800.1%
 
2225730.1%
 
2085700.1%
 
2335540.1%
 
2205490.1%
 
Other values (3275)7023214.2%
 
ValueCountFrequency (%) 
011534223.3%
 
12570.1%
 
44< 0.1%
 
512< 0.1%
 
667< 0.1%
 
7104< 0.1%
 
810450.2%
 
9155< 0.1%
 
10174< 0.1%
 
1119< 0.1%
 
ValueCountFrequency (%) 
6933756401< 0.1%
 
513567821< 0.1%
 
51338771< 0.1%
 
513387630< 0.1%
 
51314247< 0.1%
 
25000581< 0.1%
 
219461922< 0.1%
 
21043801< 0.1%
 
7152401< 0.1%
 
5017606< 0.1%
 

Atr-40
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count101
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05741178494797782
Minimum0.0
Maximum1.0
Zeros459804
Zeros (%)93.1%
Memory size3.8 MiB
2020-08-25T01:28:52.653111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2301405424
Coefficient of variation (CV)4.008594099
Kurtosis12.54252472
Mean0.05741178495
Median Absolute Deviation (MAD)0
Skewness3.805769303
Sum28362.57
Variance0.05296466923
2020-08-25T01:28:52.766225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
045980493.1%
 
1256955.2%
 
0.0118510.4%
 
0.048300.2%
 
0.027830.2%
 
0.057730.2%
 
0.035280.1%
 
0.983220.1%
 
0.992940.1%
 
0.06246< 0.1%
 
0.97147< 0.1%
 
0.95108< 0.1%
 
0.96103< 0.1%
 
0.94100< 0.1%
 
0.9399< 0.1%
 
0.0794< 0.1%
 
0.8986< 0.1%
 
0.876< 0.1%
 
0.7874< 0.1%
 
0.9168< 0.1%
 
0.7463< 0.1%
 
0.9261< 0.1%
 
0.8560< 0.1%
 
0.8752< 0.1%
 
0.8252< 0.1%
 
Other values (76)16510.3%
 
ValueCountFrequency (%) 
045980493.1%
 
0.0118510.4%
 
0.027830.2%
 
0.035280.1%
 
0.048300.2%
 
0.057730.2%
 
0.06246< 0.1%
 
0.0794< 0.1%
 
0.0843< 0.1%
 
0.0931< 0.1%
 
ValueCountFrequency (%) 
1256955.2%
 
0.992940.1%
 
0.983220.1%
 
0.97147< 0.1%
 
0.96103< 0.1%
 
0.95108< 0.1%
 
0.94100< 0.1%
 
0.9399< 0.1%
 
0.9261< 0.1%
 
0.9168< 0.1%
 

Atr-19
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
494020
ValueCountFrequency (%) 
0494020100.0%
 

Atr-39
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count101
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.058117728027205384
Minimum0.0
Maximum1.0
Zeros458791
Zeros (%)92.9%
Memory size3.8 MiB
2020-08-25T01:28:52.888418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2305897256
Coefficient of variation (CV)3.967631452
Kurtosis12.3826855
Mean0.05811772803
Median Absolute Deviation (MAD)0
Skewness3.781290254
Sum28711.32
Variance0.05317162156
2020-08-25T01:28:53.003155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
045879192.9%
 
1260405.3%
 
0.0115960.3%
 
0.029320.2%
 
0.048010.2%
 
0.057500.2%
 
0.035650.1%
 
0.06238< 0.1%
 
0.85131< 0.1%
 
0.93116< 0.1%
 
0.95115< 0.1%
 
0.87108< 0.1%
 
0.92103< 0.1%
 
0.0797< 0.1%
 
0.8497< 0.1%
 
0.0897< 0.1%
 
0.8696< 0.1%
 
0.8988< 0.1%
 
0.5287< 0.1%
 
0.9681< 0.1%
 
0.8281< 0.1%
 
0.9180< 0.1%
 
0.979< 0.1%
 
0.8875< 0.1%
 
0.0968< 0.1%
 
Other values (76)27080.5%
 
ValueCountFrequency (%) 
045879192.9%
 
0.0115960.3%
 
0.029320.2%
 
0.035650.1%
 
0.048010.2%
 
0.057500.2%
 
0.06238< 0.1%
 
0.0797< 0.1%
 
0.0897< 0.1%
 
0.0968< 0.1%
 
ValueCountFrequency (%) 
1260405.3%
 
0.9966< 0.1%
 
0.9864< 0.1%
 
0.9767< 0.1%
 
0.9681< 0.1%
 
0.95115< 0.1%
 
0.9463< 0.1%
 
0.93116< 0.1%
 
0.92103< 0.1%
 
0.9180< 0.1%
 

Atr-29
Real number (ℝ≥0)

ZEROS

Distinct count78
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020982429861139233
Minimum0.0
Maximum1.0
Zeros382020
Zeros (%)77.3%
Memory size3.8 MiB
2020-08-25T01:28:53.139566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08220557068
Coefficient of variation (CV)3.917828927
Kurtosis105.1126927
Mean0.02098242986
Median Absolute Deviation (MAD)0
Skewness9.642416922
Sum10365.74
Variance0.00675775585
2020-08-25T01:28:53.259787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
038202077.3%
 
0.065281210.7%
 
0.07287985.8%
 
0.05192183.9%
 
0.0832540.7%
 
123580.5%
 
0.049550.2%
 
0.676640.1%
 
0.55900.1%
 
0.094020.1%
 
0.62730.1%
 
0.12228< 0.1%
 
0.1209< 0.1%
 
0.11199< 0.1%
 
0.14148< 0.1%
 
0.4123< 0.1%
 
0.13116< 0.1%
 
0.29111< 0.1%
 
0.01105< 0.1%
 
0.15102< 0.1%
 
0.0398< 0.1%
 
0.3397< 0.1%
 
0.1795< 0.1%
 
0.2595< 0.1%
 
0.7571< 0.1%
 
Other values (53)8790.2%
 
ValueCountFrequency (%) 
038202077.3%
 
0.01105< 0.1%
 
0.0260< 0.1%
 
0.0398< 0.1%
 
0.049550.2%
 
0.05192183.9%
 
0.065281210.7%
 
0.07287985.8%
 
0.0832540.7%
 
0.094020.1%
 
ValueCountFrequency (%) 
123580.5%
 
0.998< 0.1%
 
0.972< 0.1%
 
0.9632< 0.1%
 
0.9517< 0.1%
 
0.893< 0.1%
 
0.881< 0.1%
 
0.861< 0.1%
 
0.831< 0.1%
 
0.821< 0.1%
 

Atr-6
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
493998
1
 
22
ValueCountFrequency (%) 
0493998> 99.9%
 
122< 0.1%
 

Atr-26
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count77
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05743352495850371
Minimum0.0
Maximum1.0
Zeros464947
Zeros (%)94.1%
Memory size3.8 MiB
2020-08-25T01:28:53.402709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2316236938
Coefficient of variation (CV)4.03290054
Kurtosis12.45743657
Mean0.05743352496
Median Absolute Deviation (MAD)0
Skewness3.798974403
Sum28373.31
Variance0.05364953553
2020-08-25T01:28:53.530130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
046494794.1%
 
1269795.5%
 
0.86113< 0.1%
 
0.87102< 0.1%
 
0.9297< 0.1%
 
0.2592< 0.1%
 
0.9591< 0.1%
 
0.991< 0.1%
 
0.578< 0.1%
 
0.9175< 0.1%
 
0.8869< 0.1%
 
0.9666< 0.1%
 
0.3365< 0.1%
 
0.265< 0.1%
 
0.9364< 0.1%
 
0.9463< 0.1%
 
0.0162< 0.1%
 
0.8961< 0.1%
 
0.8559< 0.1%
 
0.9949< 0.1%
 
0.8244< 0.1%
 
0.7738< 0.1%
 
0.1737< 0.1%
 
0.9736< 0.1%
 
0.0235< 0.1%
 
Other values (52)5420.1%
 
ValueCountFrequency (%) 
046494794.1%
 
0.0162< 0.1%
 
0.0235< 0.1%
 
0.0328< 0.1%
 
0.0413< 0.1%
 
0.0520< 0.1%
 
0.0618< 0.1%
 
0.0711< 0.1%
 
0.0814< 0.1%
 
0.0912< 0.1%
 
ValueCountFrequency (%) 
1269795.5%
 
0.9949< 0.1%
 
0.9834< 0.1%
 
0.9736< 0.1%
 
0.9666< 0.1%
 
0.9591< 0.1%
 
0.9463< 0.1%
 
0.9364< 0.1%
 
0.9297< 0.1%
 
0.9175< 0.1%
 

Atr-12
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct count23
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01021213716043885
Minimum0
Maximum884
Zeros491796
Zeros (%)99.5%
Memory size3.8 MiB
2020-08-25T01:28:53.652853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.798328078
Coefficient of variation (CV)176.0971332
Kurtosis188120.9669
Mean0.01021213716
Median Absolute Deviation (MAD)0
Skewness417.5298055
Sum5045
Variance3.233983875
2020-08-25T01:28:53.769341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
049179699.5%
 
121510.4%
 
224< 0.1%
 
416< 0.1%
 
311< 0.1%
 
63< 0.1%
 
52< 0.1%
 
72< 0.1%
 
121< 0.1%
 
91< 0.1%
 
111< 0.1%
 
7671< 0.1%
 
2381< 0.1%
 
161< 0.1%
 
181< 0.1%
 
2751< 0.1%
 
211< 0.1%
 
221< 0.1%
 
2811< 0.1%
 
381< 0.1%
 
1021< 0.1%
 
8841< 0.1%
 
131< 0.1%
 
ValueCountFrequency (%) 
049179699.5%
 
121510.4%
 
224< 0.1%
 
311< 0.1%
 
416< 0.1%
 
52< 0.1%
 
63< 0.1%
 
72< 0.1%
 
91< 0.1%
 
111< 0.1%
 
ValueCountFrequency (%) 
8841< 0.1%
 
7671< 0.1%
 
2811< 0.1%
 
2751< 0.1%
 
2381< 0.1%
 
1021< 0.1%
 
381< 0.1%
 
221< 0.1%
 
211< 0.1%
 
181< 0.1%
 

Atr-17
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
493969
1
 
48
2
 
3
ValueCountFrequency (%) 
0493969> 99.9%
 
148< 0.1%
 
23< 0.1%
 
2020-08-25T01:28:56.073372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0493969> 99.9%
 
148< 0.1%
 
23< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number494020100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0493969> 99.9%
 
148< 0.1%
 
23< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common494020100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0493969> 99.9%
 
148< 0.1%
 
23< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII494020100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0493969> 99.9%
 
148< 0.1%
 
23< 0.1%
 

Atr-9
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count22
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03451884539087487
Minimum0
Maximum30
Zeros490828
Zeros (%)99.4%
Memory size3.8 MiB
2020-08-25T01:28:56.194012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.782103372
Coefficient of variation (CV)22.65728657
Kurtosis1127.014947
Mean0.03451884539
Median Absolute Deviation (MAD)0
Skewness32.62911204
Sum17053
Variance0.6116856844
2020-08-25T01:28:56.506820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
049082899.4%
 
221920.4%
 
282740.1%
 
12560.1%
 
4112< 0.1%
 
6104< 0.1%
 
551< 0.1%
 
338< 0.1%
 
1437< 0.1%
 
3028< 0.1%
 
2228< 0.1%
 
1923< 0.1%
 
1813< 0.1%
 
2413< 0.1%
 
2010< 0.1%
 
75< 0.1%
 
172< 0.1%
 
122< 0.1%
 
151< 0.1%
 
161< 0.1%
 
101< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
049082899.4%
 
12560.1%
 
221920.4%
 
338< 0.1%
 
4112< 0.1%
 
551< 0.1%
 
6104< 0.1%
 
75< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
3028< 0.1%
 
282740.1%
 
2413< 0.1%
 
2228< 0.1%
 
2010< 0.1%
 
1923< 0.1%
 
1813< 0.1%
 
172< 0.1%
 
161< 0.1%
 
151< 0.1%
 

Atr-15
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct count20
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011351767134933808
Minimum0
Maximum993
Zeros493435
Zeros (%)99.9%
Memory size3.8 MiB
2020-08-25T01:28:56.639964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum993
Range993
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.012720363
Coefficient of variation (CV)177.3045852
Kurtosis188932.6641
Mean0.01135176713
Median Absolute Deviation (MAD)0
Skewness417.0654138
Sum5608
Variance4.051043258
2020-08-25T01:28:56.779292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
049343599.9%
 
1233< 0.1%
 
9167< 0.1%
 
6126< 0.1%
 
222< 0.1%
 
512< 0.1%
 
410< 0.1%
 
33< 0.1%
 
1191< 0.1%
 
71< 0.1%
 
9931< 0.1%
 
2681< 0.1%
 
141< 0.1%
 
161< 0.1%
 
2781< 0.1%
 
391< 0.1%
 
3061< 0.1%
 
541< 0.1%
 
8571< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
049343599.9%
 
1233< 0.1%
 
222< 0.1%
 
33< 0.1%
 
410< 0.1%
 
512< 0.1%
 
6126< 0.1%
 
71< 0.1%
 
9167< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
9931< 0.1%
 
8571< 0.1%
 
3061< 0.1%
 
2781< 0.1%
 
2681< 0.1%
 
1191< 0.1%
 
541< 0.1%
 
391< 0.1%
 
161< 0.1%
 
141< 0.1%
 

Atr-32
Real number (ℝ≥0)

Distinct count256
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.66605198170114
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Memory size3.8 MiB
2020-08-25T01:28:56.949646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q146
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)209

Descriptive statistics

Standard deviation106.0402047
Coefficient of variation (CV)0.5620523861
Kurtosis-0.8741279302
Mean188.666052
Median Absolute Deviation (MAD)0
Skewness-1.03486554
Sum93204803
Variance11244.52501
2020-08-25T01:28:57.079543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25533774668.4%
 
1118952.4%
 
272431.5%
 
358551.2%
 
1156271.1%
 
855791.1%
 
1055501.1%
 
554941.1%
 
653941.1%
 
1253881.1%
 
453821.1%
 
752651.1%
 
952611.1%
 
1352441.1%
 
1852091.1%
 
1751741.0%
 
1651021.0%
 
2050961.0%
 
1450951.0%
 
1550411.0%
 
1950111.0%
 
2112280.2%
 
2310770.2%
 
2210740.2%
 
2510720.2%
 
Other values (231)369187.5%
 
ValueCountFrequency (%) 
02< 0.1%
 
1118952.4%
 
272431.5%
 
358551.2%
 
453821.1%
 
554941.1%
 
653941.1%
 
752651.1%
 
855791.1%
 
952611.1%
 
ValueCountFrequency (%) 
25533774668.4%
 
25410670.2%
 
2536850.1%
 
2525130.1%
 
2516280.1%
 
2507210.1%
 
2496000.1%
 
2483890.1%
 
2473920.1%
 
2464080.1%
 

Atr-16
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count18
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010829521072021377
Minimum0
Maximum28
Zeros493755
Zeros (%)99.9%
Memory size3.8 MiB
2020-08-25T01:28:57.218995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09641597626
Coefficient of variation (CV)89.03069269
Kurtosis43583.80671
Mean0.001082952107
Median Absolute Deviation (MAD)0
Skewness192.334571
Sum535
Variance0.009296040477
2020-08-25T01:28:57.331641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
049375599.9%
 
1207< 0.1%
 
236< 0.1%
 
47< 0.1%
 
162< 0.1%
 
91< 0.1%
 
51< 0.1%
 
71< 0.1%
 
81< 0.1%
 
281< 0.1%
 
251< 0.1%
 
121< 0.1%
 
141< 0.1%
 
151< 0.1%
 
201< 0.1%
 
211< 0.1%
 
221< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
049375599.9%
 
1207< 0.1%
 
236< 0.1%
 
47< 0.1%
 
51< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
101< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
281< 0.1%
 
251< 0.1%
 
221< 0.1%
 
211< 0.1%
 
201< 0.1%
 
162< 0.1%
 
151< 0.1%
 
141< 0.1%
 
121< 0.1%
 
101< 0.1%
 

Atr-35
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count101
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6019359742520545
Minimum0.0
Maximum1.0
Zeros142859
Zeros (%)28.9%
Memory size3.8 MiB
2020-08-25T01:28:57.453908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4813089764
Coefficient of variation (CV)0.7996016138
Kurtosis-1.819346634
Mean0.6019359743
Median Absolute Deviation (MAD)0
Skewness-0.4006012844
Sum297368.41
Variance0.2316583308
2020-08-25T01:28:57.568627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
128888358.5%
 
014285928.9%
 
0.01219124.4%
 
0.0272281.5%
 
0.0344190.9%
 
0.0427490.6%
 
0.0521760.4%
 
0.0617840.4%
 
0.516420.3%
 
0.0814280.3%
 
0.3314130.3%
 
0.0714120.3%
 
0.2512740.3%
 
0.211510.2%
 
0.1710340.2%
 
0.119550.2%
 
0.129460.2%
 
0.149260.2%
 
0.19150.2%
 
0.098440.2%
 
0.996130.1%
 
0.984660.1%
 
0.962850.1%
 
0.952490.1%
 
0.97220< 0.1%
 
Other values (76)62371.3%
 
ValueCountFrequency (%) 
014285928.9%
 
0.01219124.4%
 
0.0272281.5%
 
0.0344190.9%
 
0.0427490.6%
 
0.0521760.4%
 
0.0617840.4%
 
0.0714120.3%
 
0.0814280.3%
 
0.098440.2%
 
ValueCountFrequency (%) 
128888358.5%
 
0.996130.1%
 
0.984660.1%
 
0.97220< 0.1%
 
0.962850.1%
 
0.952490.1%
 
0.94113< 0.1%
 
0.93151< 0.1%
 
0.92118< 0.1%
 
0.91116< 0.1%
 

Atr-34
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030905793287721146
Minimum0.0
Maximum1.0
Zeros347030
Zeros (%)70.2%
Memory size3.8 MiB
2020-08-25T01:28:57.695033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.08
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.109259214
Coefficient of variation (CV)3.535234088
Kurtosis50.29015665
Mean0.03090579329
Median Absolute Deviation (MAD)0
Skewness6.85716151
Sum15268.08
Variance0.01193757583
2020-08-25T01:28:57.810525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
034703070.2%
 
0.07459229.3%
 
0.06282245.7%
 
0.05184663.7%
 
0.08145402.9%
 
0.01133412.7%
 
0.0245430.9%
 
0.0939090.8%
 
0.0335190.7%
 
0.0428110.6%
 
120130.4%
 
0.857380.1%
 
0.865690.1%
 
0.845360.1%
 
0.14920.1%
 
0.874240.1%
 
0.123100.1%
 
0.112870.1%
 
0.64214< 0.1%
 
0.82199< 0.1%
 
0.83191< 0.1%
 
0.65189< 0.1%
 
0.14183< 0.1%
 
0.62182< 0.1%
 
0.67180< 0.1%
 
Other values (76)50081.0%
 
ValueCountFrequency (%) 
034703070.2%
 
0.01133412.7%
 
0.0245430.9%
 
0.0335190.7%
 
0.0428110.6%
 
0.05184663.7%
 
0.06282245.7%
 
0.07459229.3%
 
0.08145402.9%
 
0.0939090.8%
 
ValueCountFrequency (%) 
120130.4%
 
0.996< 0.1%
 
0.9812< 0.1%
 
0.9711< 0.1%
 
0.9611< 0.1%
 
0.9514< 0.1%
 
0.9410< 0.1%
 
0.938< 0.1%
 
0.927< 0.1%
 
0.9161< 0.1%
 

target
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count23
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.546281527063682
Minimum0
Maximum22
Zeros2203
Zeros (%)0.4%
Memory size3.8 MiB
2020-08-25T01:28:57.932495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q111
median18
Q318
95-th percentile18
Maximum22
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.161398616
Coefficient of variation (CV)0.2860798898
Kurtosis-1.170276203
Mean14.54628153
Median Absolute Deviation (MAD)0
Skewness-0.5249225756
Sum7186154
Variance17.31723844
2020-08-25T01:28:58.040322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1828079056.8%
 
910720121.7%
 
119727719.7%
 
022030.4%
 
1715890.3%
 
512470.3%
 
1510400.2%
 
2110200.2%
 
209790.2%
 
142640.1%
 
10231< 0.1%
 
353< 0.1%
 
130< 0.1%
 
621< 0.1%
 
2220< 0.1%
 
412< 0.1%
 
1610< 0.1%
 
79< 0.1%
 
28< 0.1%
 
87< 0.1%
 
134< 0.1%
 
123< 0.1%
 
192< 0.1%
 
ValueCountFrequency (%) 
022030.4%
 
130< 0.1%
 
28< 0.1%
 
353< 0.1%
 
412< 0.1%
 
512470.3%
 
621< 0.1%
 
79< 0.1%
 
87< 0.1%
 
910720121.7%
 
ValueCountFrequency (%) 
2220< 0.1%
 
2110200.2%
 
209790.2%
 
192< 0.1%
 
1828079056.8%
 
1715890.3%
 
1610< 0.1%
 
1510400.2%
 
142640.1%
 
134< 0.1%
 

Interactions

2020-08-25T01:27:38.574359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:38.874315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:39.169439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:39.461131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:39.754801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:40.060610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:40.350060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:40.636600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:40.916908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:41.206513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:41.489722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:41.781873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:42.058406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:42.337059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:42.614445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:42.894729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:43.174905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:43.470495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:43.779567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:44.065848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:44.369371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:44.649874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:44.937215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:45.231805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:45.542599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:45.834758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:46.119943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:46.405823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:46.685245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:46.974565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:47.474210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:47.776147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:48.093217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:48.404902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:48.727075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:49.033234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:49.341764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:49.655338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:49.954911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:50.261487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:50.572532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:50.914464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:51.216478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:51.520363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:51.826116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:27:52.123305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:28:05.391241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:05.670054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:05.973166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:06.468336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:06.743831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:07.022931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:07.299541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:07.595025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:07.878155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:08.165288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:08.475345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:08.775584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:09.059551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:09.346828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:09.646616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:09.939375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:10.223716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:10.513436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:10.794412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:11.070326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:11.353337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:11.633475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:11.914742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:12.192286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:12.476771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:12.776491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:13.066957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:13.343204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:13.622445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:13.902655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:14.171535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:14.455468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:14.753330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:15.043918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:15.337339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:15.830197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:16.127646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:16.423194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:16.740474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:17.041053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:17.338587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:17.633203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:17.933956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:18.225553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:18.541488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:18.840867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:19.131385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:19.427044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:19.723854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:20.004606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:20.284881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:20.568031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:20.852114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:21.142114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:21.431151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:21.739543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:22.038227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:22.339402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:22.643855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:22.934757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:23.227921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:23.517572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:23.832015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:24.139465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:24.429213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:24.713690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:25.004325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:25.513237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:25.797854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:26.089345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:26.388872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:26.686940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:26.968746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:27.280760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:27.593056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:27.910488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:28.202086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:28.513414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:28.845012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:29.156823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:29.477349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:29.803168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:30.108918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:30.396434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:30.695217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:31.012254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:31.319531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:31.634176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:31.938862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:32.234608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:32.528683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:32.833654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:33.152016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:33.478051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:33.792996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:34.090831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:34.408567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:34.727897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:35.024241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:35.533628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:35.840356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:36.143591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:36.441588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:36.734085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:37.019094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:37.298897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:37.600889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:37.898833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:38.190715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:38.486734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:38.791121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:39.083893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:39.376555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:39.680896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:40.010871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:40.321738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:40.633148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:40.939888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:41.242749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:41.535180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:41.814848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:42.101746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:42.389396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:42.668731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:42.957489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:43.239574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:43.537141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:43.833642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:44.130546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:44.423078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:44.715384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:45.213413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:45.496510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:45.778354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:46.065437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:28:58.184580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:28:58.520723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:28:58.854563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:28:59.196109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:28:59.455626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:28:46.555267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:28:47.742003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Atr-24Atr-13Atr-8Atr-25Atr-4Atr-40Atr-19Atr-39Atr-29Atr-6Atr-26Atr-12Atr-17Atr-9Atr-15Atr-32Atr-16Atr-35Atr-34target
00.0000.01810.000.00.000.00000900.110.011
10.0000.02390.000.00.000.000001900.050.011
20.0000.02350.000.00.000.000002900.030.011
30.0000.02190.000.00.000.000003900.030.011
40.0000.02170.000.00.000.000004900.020.011
50.0000.02170.000.00.000.000005900.020.011
60.0000.02120.000.00.000.000006901.000.011
70.0000.01590.000.00.000.000007900.090.011
80.0000.02100.000.00.000.000008900.120.011
90.0000.02120.000.00.000.000109900.120.011

Last rows

Atr-24Atr-13Atr-8Atr-25Atr-4Atr-40Atr-19Atr-39Atr-29Atr-6Atr-26Atr-12Atr-17Atr-9Atr-15Atr-32Atr-16Atr-35Atr-34target
4940100.00000.003080.000.00.000.0000025500.030.011
4940110.00000.002910.000.00.000.0000025500.020.011
4940120.00000.002890.000.00.000.0000025500.020.011
4940130.00000.003060.000.00.000.0000025500.020.011
4940140.00000.002890.000.00.000.0000025500.010.011
4940150.00000.003100.000.00.000.0000025500.010.011
4940160.00000.002820.000.00.000.0000025500.170.011
4940170.17000.112030.000.00.000.0000025500.060.011
4940180.00000.002910.000.00.000.0000025500.040.011
4940190.00000.002190.000.00.000.0000025500.170.011

Duplicate rows

Most frequent

Atr-24Atr-13Atr-8Atr-25Atr-4Atr-40Atr-19Atr-39Atr-29Atr-6Atr-26Atr-12Atr-17Atr-9Atr-15Atr-32Atr-16Atr-35Atr-34targetcount
69330.0000.010320.000.00.0000.0000025501.00.0018227840
69150.0000.05200.000.00.0000.0000025501.00.001852747
76931.0001.000.000.00.0600.000001800.00.0791143
77051.0001.000.000.00.0600.000002000.00.0791126
76581.0001.000.000.00.0600.000001200.00.0791105
76751.0001.000.000.00.0600.000001500.00.0791095
76441.0001.000.000.00.0600.000001000.00.0791088
76871.0001.000.000.00.0600.000001700.00.0791075
76631.0001.000.000.00.0600.000001300.00.0791064
76691.0001.000.000.00.0600.000001400.00.0791053